A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

Construction of 1-Resilient Boolean Functions with Optimal Algebraic Immunity and Good Nonlinearity

This paper presents a construction for a class of 1-resilient functions with optimal algebraic immunity on an even number of variables. The construction is based on the concatenation of two balanced functions in associative classes. For some n, a part of 1-resilient functions with maximum algebraic immunity constructed in the paper can achieve almost optimal nonlinearity. Apart from their high nonlinearity, the functions reach Siegenthaler's upper bound of algebraic degree. Also a class of 1-resilient functions on any number n > 2 of variables with at least sub-optimal algebraic immunity is provided.     

一种家庭异构网络系统终端设计

针对目前家庭通信网络日益复杂化和多样化问题,提出了一种新型的基于Wire-lessHART和PLC的无线自组织网络系统智能终端结构。通过兼容电力线通信、WirelessHART协议以及以太网的方法,实现了异构型网络通信、智能化控制家电等功能。     

交流阻抗法测定锂离子电池正极材料的导电率

采用交流阻抗法测定了3种锂离子电池正极材料LiCoO2,LiMn2O4和LiFePO4的电子导电率、离子导电率和材料电导率.结果表明,LiCoO2材料的电导率较高,含有聚阴离子基团LiFePO4材料的电子电导率和离子电导率均较低.     

合成氨工艺参数优化技术开发及应用

以氨净值为响应值,影响响应值的17个过程参数为变量,经逐步回归选入触媒温度左3,触媒温度左4,循环CH_4%,H/N,NH_3%(进口)5个过程参数建立预测方程,并根据该方程对响应值的拟合结果和误差分布对样本进行分类。在此基础上,采取图形显示技术和统计分析的方法对分类样本集的过程参数进行比对分析,由此获取优类信息制订优化的工艺参数方案,经仿真验证后用于指导合成氨装置的生产试验。试验结果与试验前的对照期比较,氨净值均值由13.86提高到14.54,吨氨气耗减少15.9887m~3,吨氨电耗减少15.8kWh,节能效益十分明显     

一种分布式网络指纹识别系统

文章介绍了一种分布式网络指纹识别系统NBFIS(Network-BasedFingerprintIdentifyingSystem)。首先提出模型框架,并对其关键技术特别是指纹图像处理技术及面向对象的数据库技术进行讨论。实现了NBFIS系统的原型并给出实验和性能测试结果。     

一种中文文档的非受限无词典抽词方法

本文提出了一种非受限无词典抽词模型,该模型通过自增长算法获取中文文档中的汉字结合模式,并引入支持度、置信度等概念来筛选词条。实验表明:在无需词典支持和利用语料库学习的前提下,该算法能够快速、准确地抽取中文文档中的中、高频词条。适于对词条频度敏感,而又对计算速度要求很高的中文信息处理应用,例如实时文档自动分类系统。     

基于双链式存储的可靠通信网合并

本文介绍图的双链式存储结构在可靠通讯网合并算法中的应用.     

基于级连分组BP网络的高精度手写数字识别

本文提出了一种基于级连分组网的手写数字识别的新方法。这种方法根据将每次分类任务简单化的思想,将每个网络的任务简化以提高其辨别能力。整个系统分为两级,第一级进行粗分类,选取前两个后选字。第二级对两个后选字进行细分类。每个细分类网络完成区分两类特定模式的任务,由于每个子网络分类数目的减少导致识别精度的提高。使用我们自己构造的含10万个字符的库进行测试,我们的系统达到了在拒识为5%以内时误识为0.067%。     

修辞视角下的因特网站名称

当网络以难以想象的高速发展的时候,由于各行各业的人们加入网络语言逐渐形成了自己的风格。作为一种新兴的媒体语言,网络语言具有经济、直接、生动、活泼等特点。该文从修辞的视角对大量的网站名称实例进行分析,并介绍网站名称的构成特点、翻译及修辞效果。